Overview

Dataset statistics

Number of variables11
Number of observations801
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.0 KiB
Average record size in memory88.2 B

Variable types

Numeric10
Categorical1

Alerts

gene_9175 is highly overall correlated with gene_9176 and 2 other fieldsHigh correlation
gene_4476 is highly overall correlated with gene_15895 and 1 other fieldsHigh correlation
gene_2298 is highly overall correlated with gene_220 and 2 other fieldsHigh correlation
gene_7964 is highly overall correlated with gene_220 and 1 other fieldsHigh correlation
gene_9176 is highly overall correlated with gene_9175 and 2 other fieldsHigh correlation
gene_220 is highly overall correlated with gene_2298 and 2 other fieldsHigh correlation
gene_15895 is highly overall correlated with gene_4476 and 1 other fieldsHigh correlation
gene_18135 is highly overall correlated with gene_9175 and 1 other fieldsHigh correlation
gene_219 is highly overall correlated with gene_2298 and 1 other fieldsHigh correlation
gene_3523 is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with gene_9175 and 6 other fieldsHigh correlation
gene_2298 has unique valuesUnique
gene_9175 has 235 (29.3%) zerosZeros
gene_4476 has 105 (13.1%) zerosZeros
gene_7964 has 186 (23.2%) zerosZeros
gene_9176 has 361 (45.1%) zerosZeros
gene_220 has 525 (65.5%) zerosZeros
gene_15895 has 566 (70.7%) zerosZeros
gene_18135 has 438 (54.7%) zerosZeros
gene_219 has 467 (58.3%) zerosZeros
gene_3523 has 31 (3.9%) zerosZeros

Reproduction

Analysis started2022-12-24 10:39:44.843828
Analysis finished2022-12-24 10:39:52.868143
Duration8.02 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

gene_9175
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct565
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7716319
Minimum0
Maximum18.252042
Zeros235
Zeros (%)29.3%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:52.916277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.0690834
Q32.7334411
95-th percentile17.202573
Maximum18.252042
Range18.252042
Interquartile range (IQR)2.7334411

Descriptive statistics

Standard deviation6.0300862
Coefficient of variation (CV)1.5988003
Kurtosis0.84547836
Mean3.7716319
Median Absolute Deviation (MAD)1.0690834
Skewness1.6207025
Sum3021.0772
Variance36.36194
MonotonicityNot monotonic
2022-12-24T05:39:52.986397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 235
29.3%
0.7194901312 2
 
0.2%
1.683920981 2
 
0.2%
0.9852818236 1
 
0.1%
1.469833892 1
 
0.1%
1.990664477 1
 
0.1%
3.374302252 1
 
0.1%
16.77640733 1
 
0.1%
2.435975226 1
 
0.1%
16.92105058 1
 
0.1%
Other values (555) 555
69.3%
ValueCountFrequency (%)
0 235
29.3%
0.2065182017 1
 
0.1%
0.2940766492 1
 
0.1%
0.3166091843 1
 
0.1%
0.3278023154 1
 
0.1%
0.3311319222 1
 
0.1%
0.3449420906 1
 
0.1%
0.346531462 1
 
0.1%
0.3494785145 1
 
0.1%
0.3523065529 1
 
0.1%
ValueCountFrequency (%)
18.25204169 1
0.1%
18.15182917 1
0.1%
18.08509377 1
0.1%
18.01111257 1
0.1%
18.01099242 1
0.1%
17.97416248 1
0.1%
17.94002677 1
0.1%
17.89770367 1
0.1%
17.79990766 1
0.1%
17.7611941 1
0.1%

gene_4476
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct697
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4245663
Minimum0
Maximum15.341022
Zeros105
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:53.059704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0247452
median5.14681
Q38.6301688
95-th percentile12.843185
Maximum15.341022
Range15.341022
Interquartile range (IQR)7.6054236

Descriptive statistics

Standard deviation4.3375787
Coefficient of variation (CV)0.79961761
Kurtosis-1.0610337
Mean5.4245663
Median Absolute Deviation (MAD)3.916853
Skewness0.35732045
Sum4345.0776
Variance18.814589
MonotonicityNot monotonic
2022-12-24T05:39:53.129361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 105
 
13.1%
6.685631847 1
 
0.1%
5.508549083 1
 
0.1%
3.543817546 1
 
0.1%
7.216736159 1
 
0.1%
0.8582197663 1
 
0.1%
4.593144583 1
 
0.1%
3.244171622 1
 
0.1%
3.08841301 1
 
0.1%
11.21859351 1
 
0.1%
Other values (687) 687
85.8%
ValueCountFrequency (%)
0 105
13.1%
0.3386811246 1
 
0.1%
0.3416442405 1
 
0.1%
0.3497049617 1
 
0.1%
0.3508366648 1
 
0.1%
0.3552418439 1
 
0.1%
0.3564819023 1
 
0.1%
0.3586213138 1
 
0.1%
0.3601956947 1
 
0.1%
0.3884650973 1
 
0.1%
ValueCountFrequency (%)
15.34102186 1
0.1%
15.30754237 1
0.1%
14.91043193 1
0.1%
14.77838519 1
0.1%
14.7103383 1
0.1%
14.63967986 1
0.1%
14.54275379 1
0.1%
14.51602007 1
0.1%
14.39886391 1
0.1%
14.28579954 1
0.1%

gene_2298
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5710307
Minimum2.567083
Maximum10.975719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:53.207494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.567083
5-th percentile5.8576352
Q18.183943
median8.773848
Q39.3545579
95-th percentile10.025707
Maximum10.975719
Range8.4086361
Interquartile range (IQR)1.170615

Descriptive statistics

Standard deviation1.2175266
Coefficient of variation (CV)0.14205136
Kurtosis3.2785009
Mean8.5710307
Median Absolute Deviation (MAD)0.58517013
Skewness-1.5901871
Sum6865.3956
Variance1.482371
MonotonicityNot monotonic
2022-12-24T05:39:53.274127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.954109048 1
 
0.1%
9.528922886 1
 
0.1%
8.498673719 1
 
0.1%
9.63811367 1
 
0.1%
3.78001607 1
 
0.1%
6.749882832 1
 
0.1%
8.307492279 1
 
0.1%
9.668954104 1
 
0.1%
9.71412357 1
 
0.1%
9.552354014 1
 
0.1%
Other values (791) 791
98.8%
ValueCountFrequency (%)
2.567082971 1
0.1%
3.516645558 1
0.1%
3.78001607 1
0.1%
4.15509346 1
0.1%
4.195284597 1
0.1%
4.230479719 1
0.1%
4.245944344 1
0.1%
4.24701605 1
0.1%
4.271500379 1
0.1%
4.317607975 1
0.1%
ValueCountFrequency (%)
10.97571902 1
0.1%
10.64887884 1
0.1%
10.64392832 1
0.1%
10.5956864 1
0.1%
10.57735329 1
0.1%
10.49828079 1
0.1%
10.47197998 1
0.1%
10.39238183 1
0.1%
10.3886416 1
0.1%
10.37453962 1
0.1%

gene_7964
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct613
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7185944
Minimum0
Maximum13.232343
Zeros186
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:53.341847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.38250126
median7.5794668
Q39.5502581
95-th percentile11.831996
Maximum13.232343
Range13.232343
Interquartile range (IQR)9.1677569

Descriptive statistics

Standard deviation4.562974
Coefficient of variation (CV)0.7979188
Kurtosis-1.6589241
Mean5.7185944
Median Absolute Deviation (MAD)3.6842895
Skewness-0.16476048
Sum4580.5941
Variance20.820732
MonotonicityNot monotonic
2022-12-24T05:39:53.416494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 186
 
23.2%
0.7278769008 2
 
0.2%
0.4776773276 2
 
0.2%
0.3564819023 2
 
0.2%
5.248777694 1
 
0.1%
0.816886374 1
 
0.1%
13.14871966 1
 
0.1%
10.08305377 1
 
0.1%
8.804072925 1
 
0.1%
10.88350627 1
 
0.1%
Other values (603) 603
75.3%
ValueCountFrequency (%)
0 186
23.2%
0.2708279156 1
 
0.1%
0.3132458518 1
 
0.1%
0.3278023154 1
 
0.1%
0.3458505172 1
 
0.1%
0.3504972471 1
 
0.1%
0.3542264574 1
 
0.1%
0.3564819023 2
 
0.2%
0.3612068902 1
 
0.1%
0.3614315041 1
 
0.1%
ValueCountFrequency (%)
13.23234297 1
0.1%
13.14871966 1
0.1%
12.79610452 1
0.1%
12.70020778 1
0.1%
12.67263749 1
0.1%
12.6577628 1
0.1%
12.62848883 1
0.1%
12.48165061 1
0.1%
12.46991649 1
0.1%
12.46926024 1
0.1%

gene_9176
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct440
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9467476
Minimum0
Maximum20.585565
Zeros361
Zeros (%)45.1%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:53.489922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.50294157
Q33.1386834
95-th percentile18.823196
Maximum20.585565
Range20.585565
Interquartile range (IQR)3.1386834

Descriptive statistics

Standard deviation6.6905791
Coefficient of variation (CV)1.6952133
Kurtosis0.72447198
Mean3.9467476
Median Absolute Deviation (MAD)0.50294157
Skewness1.5774553
Sum3161.3448
Variance44.763849
MonotonicityNot monotonic
2022-12-24T05:39:53.555337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 361
45.1%
0.8358432487 2
 
0.2%
1.423363028 1
 
0.1%
0.7806460588 1
 
0.1%
8.704778612 1
 
0.1%
0.9325147777 1
 
0.1%
15.91136858 1
 
0.1%
16.38697223 1
 
0.1%
1.304277388 1
 
0.1%
0.4158668105 1
 
0.1%
Other values (430) 430
53.7%
ValueCountFrequency (%)
0 361
45.1%
0.2813124909 1
 
0.1%
0.2839217723 1
 
0.1%
0.2940766492 1
 
0.1%
0.3154503 1
 
0.1%
0.3242345623 1
 
0.1%
0.3305584 1
 
0.1%
0.3335382288 1
 
0.1%
0.3386811246 1
 
0.1%
0.3405053007 1
 
0.1%
ValueCountFrequency (%)
20.585565 1
0.1%
19.8228322 1
0.1%
19.76422854 1
0.1%
19.63480751 1
0.1%
19.59748156 1
0.1%
19.5932744 1
0.1%
19.57147306 1
0.1%
19.51817481 1
0.1%
19.46957028 1
0.1%
19.4676752 1
0.1%

gene_220
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct276
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0902075
Minimum0
Maximum13.944996
Zeros525
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:53.623109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.70283482
95-th percentile11.887986
Maximum13.944996
Range13.944996
Interquartile range (IQR)0.70283482

Descriptive statistics

Standard deviation4.1839288
Coefficient of variation (CV)2.0016811
Kurtosis1.1953034
Mean2.0902075
Median Absolute Deviation (MAD)0
Skewness1.736265
Sum1674.2562
Variance17.50526
MonotonicityNot monotonic
2022-12-24T05:39:53.690488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 525
65.5%
0.4119685104 2
 
0.2%
12.03360539 1
 
0.1%
11.63059932 1
 
0.1%
12.58983838 1
 
0.1%
0.4315694761 1
 
0.1%
10.6637182 1
 
0.1%
0.4655043696 1
 
0.1%
10.09541021 1
 
0.1%
0.5940707569 1
 
0.1%
Other values (266) 266
33.2%
ValueCountFrequency (%)
0 525
65.5%
0.2738142446 1
 
0.1%
0.3106891175 1
 
0.1%
0.3343394399 1
 
0.1%
0.3368546391 1
 
0.1%
0.3405053007 1
 
0.1%
0.3438057523 1
 
0.1%
0.3491387771 1
 
0.1%
0.3492520318 1
 
0.1%
0.3564819023 1
 
0.1%
ValueCountFrequency (%)
13.94499567 1
0.1%
13.64190498 1
0.1%
13.55778676 1
0.1%
13.46747819 1
0.1%
13.34906002 1
0.1%
13.15195836 1
0.1%
13.14050548 1
0.1%
13.13803176 1
0.1%
12.97268189 1
0.1%
12.9610499 1
0.1%

gene_15895
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct235
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0325949
Minimum0
Maximum14.019113
Zeros566
Zeros (%)70.7%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:53.764745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.69403327
95-th percentile11.798476
Maximum14.019113
Range14.019113
Interquartile range (IQR)0.69403327

Descriptive statistics

Standard deviation4.0905451
Coefficient of variation (CV)2.0124743
Kurtosis1.3423911
Mean2.0325949
Median Absolute Deviation (MAD)0
Skewness1.765213
Sum1628.1085
Variance16.73256
MonotonicityNot monotonic
2022-12-24T05:39:53.832956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 566
70.7%
0.550605202 2
 
0.2%
12.48863099 1
 
0.1%
11.67952848 1
 
0.1%
0.3884650973 1
 
0.1%
11.35550527 1
 
0.1%
10.6108772 1
 
0.1%
9.00864431 1
 
0.1%
11.42798846 1
 
0.1%
1.826762016 1
 
0.1%
Other values (225) 225
 
28.1%
ValueCountFrequency (%)
0 566
70.7%
0.3601956947 1
 
0.1%
0.3673710656 1
 
0.1%
0.3884650973 1
 
0.1%
0.4002100071 1
 
0.1%
0.402613043 1
 
0.1%
0.4061012382 1
 
0.1%
0.4293213699 1
 
0.1%
0.4408459182 1
 
0.1%
0.4514353304 1
 
0.1%
ValueCountFrequency (%)
14.01911288 1
0.1%
13.43999966 1
0.1%
12.91168971 1
0.1%
12.91030544 1
0.1%
12.90776008 1
0.1%
12.84556062 1
0.1%
12.7679775 1
0.1%
12.70669458 1
0.1%
12.68150856 1
0.1%
12.66653692 1
0.1%

gene_18135
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct359
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1529635
Minimum0
Maximum14.832405
Zeros438
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:53.907614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.0842004
95-th percentile12.474877
Maximum14.832405
Range14.832405
Interquartile range (IQR)1.0842004

Descriptive statistics

Standard deviation4.1836984
Coefficient of variation (CV)1.9432278
Kurtosis1.8172963
Mean2.1529635
Median Absolute Deviation (MAD)0
Skewness1.8640997
Sum1724.5237
Variance17.503333
MonotonicityNot monotonic
2022-12-24T05:39:53.976593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 438
54.7%
0.416407409 2
 
0.2%
0.7629015479 2
 
0.2%
0.3564819023 2
 
0.2%
0.3851548969 2
 
0.2%
1.641453546 2
 
0.2%
6.878308459 1
 
0.1%
12.60242514 1
 
0.1%
0.816886374 1
 
0.1%
0.540324906 1
 
0.1%
Other values (349) 349
43.6%
ValueCountFrequency (%)
0 438
54.7%
0.2813124909 1
 
0.1%
0.2839217723 1
 
0.1%
0.3109217352 1
 
0.1%
0.3118518312 1
 
0.1%
0.3166091843 1
 
0.1%
0.3305584 1
 
0.1%
0.3335382288 1
 
0.1%
0.3377681709 1
 
0.1%
0.341530387 1
 
0.1%
ValueCountFrequency (%)
14.83240548 1
0.1%
14.79529393 1
0.1%
14.49249165 1
0.1%
14.45367576 1
0.1%
14.45283323 1
0.1%
14.37191274 1
0.1%
14.20881352 1
0.1%
14.2025371 1
0.1%
14.04435996 1
0.1%
13.97128308 1
0.1%

gene_219
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct332
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2735455
Minimum0
Maximum14.089633
Zeros467
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:54.049160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.001442
95-th percentile12.293874
Maximum14.089633
Range14.089633
Interquartile range (IQR)1.001442

Descriptive statistics

Standard deviation4.3396632
Coefficient of variation (CV)1.9087646
Kurtosis1.0404812
Mean2.2735455
Median Absolute Deviation (MAD)0
Skewness1.691134
Sum1821.11
Variance18.832677
MonotonicityNot monotonic
2022-12-24T05:39:54.113967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
58.3%
0.3851548969 2
 
0.2%
0.3458505172 2
 
0.2%
0.3614315041 2
 
0.2%
12.5124837 1
 
0.1%
11.29389118 1
 
0.1%
12.44859644 1
 
0.1%
0.4315694761 1
 
0.1%
0.5225579929 1
 
0.1%
10.80136886 1
 
0.1%
Other values (322) 322
40.2%
ValueCountFrequency (%)
0 467
58.3%
0.2910141014 1
 
0.1%
0.3237735637 1
 
0.1%
0.3311319222 1
 
0.1%
0.3335382288 1
 
0.1%
0.341074883 1
 
0.1%
0.341530387 1
 
0.1%
0.3438057523 1
 
0.1%
0.3458505172 2
 
0.2%
0.3492520318 1
 
0.1%
ValueCountFrequency (%)
14.08963254 1
0.1%
13.78496195 1
0.1%
13.72317029 1
0.1%
13.72011598 1
0.1%
13.42966827 1
0.1%
13.35183644 1
0.1%
13.31708752 1
0.1%
13.26920705 1
0.1%
13.23962985 1
0.1%
13.215762 1
0.1%

gene_3523
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct766
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0769436
Minimum0
Maximum12.867153
Zeros31
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-12-24T05:39:54.184677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.41640741
Q11.3735647
median2.2202371
Q33.2777914
95-th percentile11.956387
Maximum12.867153
Range12.867153
Interquartile range (IQR)1.9042267

Descriptive statistics

Standard deviation3.0890216
Coefficient of variation (CV)1.0039253
Kurtosis3.6750399
Mean3.0769436
Median Absolute Deviation (MAD)0.94011218
Skewness2.1484835
Sum2464.6319
Variance9.5420542
MonotonicityNot monotonic
2022-12-24T05:39:54.258211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
3.9%
2.233796049 2
 
0.2%
3.211713117 2
 
0.2%
1.868884273 2
 
0.2%
3.43187698 2
 
0.2%
0.5457707261 2
 
0.2%
3.107034994 1
 
0.1%
1.007769594 1
 
0.1%
11.86099383 1
 
0.1%
11.60129724 1
 
0.1%
Other values (756) 756
94.4%
ValueCountFrequency (%)
0 31
3.9%
0.2836847598 1
 
0.1%
0.3727292707 1
 
0.1%
0.3815048826 1
 
0.1%
0.3848234587 1
 
0.1%
0.3913278068 1
 
0.1%
0.3997726611 1
 
0.1%
0.4003193229 1
 
0.1%
0.4144603058 1
 
0.1%
0.4160830742 1
 
0.1%
ValueCountFrequency (%)
12.86715323 1
0.1%
12.80988065 1
0.1%
12.74179981 1
0.1%
12.69287699 1
0.1%
12.63499931 1
0.1%
12.62002275 1
0.1%
12.51684004 1
0.1%
12.50949544 1
0.1%
12.4956427 1
0.1%
12.46563527 1
0.1%

Class
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
BRCA
300 
KIRC
146 
LUAD
141 
PRAD
136 
COAD
78 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3204
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRAD
2nd rowLUAD
3rd rowPRAD
4th rowPRAD
5th rowBRCA

Common Values

ValueCountFrequency (%)
BRCA 300
37.5%
KIRC 146
18.2%
LUAD 141
17.6%
PRAD 136
17.0%
COAD 78
 
9.7%

Length

2022-12-24T05:39:54.322584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-24T05:39:54.384828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
brca 300
37.5%
kirc 146
18.2%
luad 141
17.6%
prad 136
17.0%
coad 78
 
9.7%

Most occurring characters

ValueCountFrequency (%)
A 655
20.4%
R 582
18.2%
C 524
16.4%
D 355
11.1%
B 300
9.4%
K 146
 
4.6%
I 146
 
4.6%
L 141
 
4.4%
U 141
 
4.4%
P 136
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3204
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 655
20.4%
R 582
18.2%
C 524
16.4%
D 355
11.1%
B 300
9.4%
K 146
 
4.6%
I 146
 
4.6%
L 141
 
4.4%
U 141
 
4.4%
P 136
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3204
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 655
20.4%
R 582
18.2%
C 524
16.4%
D 355
11.1%
B 300
9.4%
K 146
 
4.6%
I 146
 
4.6%
L 141
 
4.4%
U 141
 
4.4%
P 136
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 655
20.4%
R 582
18.2%
C 524
16.4%
D 355
11.1%
B 300
9.4%
K 146
 
4.6%
I 146
 
4.6%
L 141
 
4.4%
U 141
 
4.4%
P 136
 
4.2%

Interactions

2022-12-24T05:39:52.029553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:44.997403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.682621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.369440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.995328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.637024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.279042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.942959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.612394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.359766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.096093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.066676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.752622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.436172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.066658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.701977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.353126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.015229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.685309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.430362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.161455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.145211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.824169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.502941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.136719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.769957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.428725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.085927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.753441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.496654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.220864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.205107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.885552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.554519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.196725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.830306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.484556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.148930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.814492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.557411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.283501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.273557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.950931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.615422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.256510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.893843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.547630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.215223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.876827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.619324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.343186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.335419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.013717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.670677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.316835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.951179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.611551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.275252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.943090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.683753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.412928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.404321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.084629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.732774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.378652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.015636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.678990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.341890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:50.013615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.750417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.480412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.474129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.153805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.805359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.446403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.082271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.748057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.407883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:50.081901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.818199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.547590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.545907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.223671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.867143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.509856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.149211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.815521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.477787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.227942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.890003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:52.614758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:45.613894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.301972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:46.938541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:47.574748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.216700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:48.880808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:49.547581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.297537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-24T05:39:51.958962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-24T05:39:54.444460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
gene_9175gene_4476gene_2298gene_7964gene_9176gene_220gene_15895gene_18135gene_219gene_3523Class
gene_91751.0000.148-0.147-0.3000.719-0.265-0.0540.501-0.225-0.0620.510
gene_44760.1481.000-0.179-0.281-0.030-0.4730.5150.087-0.4420.1590.532
gene_2298-0.147-0.1791.0000.256-0.2030.511-0.055-0.0370.535-0.3320.502
gene_7964-0.300-0.2810.2561.000-0.1430.5100.2890.0600.483-0.1270.674
gene_91760.719-0.030-0.203-0.1431.000-0.196-0.1420.539-0.166-0.0060.540
gene_220-0.265-0.4730.5110.510-0.1961.000-0.183-0.1020.760-0.2880.487
gene_15895-0.0540.515-0.0550.289-0.142-0.1831.0000.146-0.1570.0260.506
gene_181350.5010.087-0.0370.0600.539-0.1020.1461.000-0.056-0.1790.497
gene_219-0.225-0.4420.5350.483-0.1660.760-0.157-0.0561.000-0.2490.487
gene_3523-0.0620.159-0.332-0.127-0.006-0.2880.026-0.179-0.2491.0000.548
Class0.5100.5320.5020.6740.5400.4870.5060.4970.4870.5481.000

Missing values

2022-12-24T05:39:52.712102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-24T05:39:52.821595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

gene_9175gene_4476gene_2298gene_7964gene_9176gene_220gene_15895gene_18135gene_219gene_3523Class
017.1735706.6856327.9541095.24877818.5251610.5918710.0000006.8783080.5918711.822037PRAD
10.00000012.6070007.7091733.8908260.0000000.00000010.0688320.0000000.0000001.327170LUAD
214.8184221.0741634.1952851.07416316.0535970.4525950.00000012.9000290.0000002.438799PRAD
317.3710797.6087217.7824412.65002918.3717940.4348820.00000013.9073041.0394191.039419PRAD
41.5800976.0650388.5803640.0000000.0000000.0000000.0000000.0000000.0000002.678342BRCA
516.0648350.0000008.6356711.19415017.3980840.5154100.00000013.9712830.0000001.442280PRAD
60.0000000.0000009.82555511.8340840.0000005.6831070.0000000.0000006.8802942.448452KIRC
715.7487481.0530429.7646466.54596817.6045600.4418020.00000012.3037291.6552600.779554PRAD
81.6839219.2672888.7164420.0000000.0000000.0000000.0000001.5540490.0000001.913148BRCA
916.7805780.0000008.2884238.65097317.8537850.0000000.00000014.2088140.0000001.851199PRAD
gene_9175gene_4476gene_2298gene_7964gene_9176gene_220gene_15895gene_18135gene_219gene_3523Class
7910.6779826.3144129.1279630.0000000.0000000.00.0000000.0000000.0000002.925753BRCA
7920.0000000.3586219.0766580.0000000.0000000.00.0000000.0000000.0000002.958286BRCA
7931.6712934.4434417.7660580.0000001.4961040.00.0000000.0000000.0000001.065228BRCA
79416.5934394.3766938.6790616.98505618.3084370.03.1768978.1319290.4169482.005867PRAD
7950.42910710.9354529.91821910.5584400.0000000.010.5192330.4291070.0000001.450063LUAD
7961.9555732.8416718.3224690.0000001.6117390.00.0000000.0000000.0000002.354001BRCA
7970.61805113.2671898.6891779.4880650.0000000.012.0059820.0000000.0000002.073340LUAD
7983.6486733.7597428.3759137.0926624.8124060.04.6774583.9117781.0025959.643669COAD
79916.4182696.3036538.2921798.32830218.1665160.03.23956612.7412560.0000000.000000PRAD
80015.60510311.0675088.1626981.60392916.8644880.00.0000008.3902331.6039293.083894PRAD